1. Field of the Invention
The present invention relates to a quality assurance (QA) program in medical imaging that uses a sensor to obtain information from a patient, the information being used to track and analyze individual and collective QA performance and patient safety measures. Thus, the present invention combines QA and quality control (QC) technology to automate and objectify QA analysis, so that all medical imaging exams (and the patients in which these exams are performed) are analyzed in an identical and reproducible manner, regardless of the specific technology and personnel acquiring the imaging data.
2. Description of the Related Art
In current medical imaging practice, accountability and verifiability of quality standards can be divided into two separate and distinct exercises. The first exercise is quality control (QC), which is largely the domain of the medical physicist and equipment manufacturer. This consists of a series of periodic surveillance tests aimed at ensuring that the physical performance capabilities of the equipment used for medical imaging acquisition (e.g., computerized tomography (CT) scanner) is in keeping with pre-defined quality standards. In this endeavor, the acquisition device is externally tested using a series of phantoms to ensure that a number of quality-centric metrics are maintained in accordance with industry-wide standards. This form of quality testing is performed independent of the patient and the specific clinical scenario in which the medical imaging application is being utilized. Simply stated, it is a “one size fits all approach” to quality assessment, in which the data is largely viewed in a vacuum.
The second form of quality assessment within medical imaging is quality assurance (QA), which is clinical in its orientation (as opposed to the technical orientation of QC). Whereas QC is the domain of the medical physicist and equipment manufacturer (i.e., technical practitioners), QA is primarily the domain of the clinical practitioners, which include the imaging technologist, radiologist, and clinician. In the practice of QA, these clinical practitioners are tasked with the assessment of image quality, and the corresponding ability to discriminate between “normal” and “pathologic” states within the medical imaging dataset. While the performance of all three stakeholders are largely affected by image quality, the principal party tasked with QA assessment in current medical practice is the imaging technologist, who performs QA assessment at the point of image acquisition (i.e., image capture), and determines whether the imaging dataset obtained is of sufficient quality (i.e., pass or fail), and can be subsequently submitted for radiologist and clinician interpretation. Unlike the case with QC, which is external to the patient and performed on a periodic basis, QA is intrinsically tied to the clinical circumstances prompting the imaging examination. As a result, the QA assessment process must be performed in the relative context of each individual patient (e.g., body habitus, mobility) and clinical indication (e.g., specific reason for testing, overall morbidity).
To a large extent, this clinical assessment of medical imaging quality (QA) is largely subjective in nature, due to the lack of reproducible objective imaging quality metrics. The one exception is mammography, which has well defined imaging quality standards as defined by the Mammography Quality Standards Act (MQSA). QA standards for the remaining medical imaging modalities are largely left to the individual discretion of the imaging provider, with the technologist performing the image acquisition playing the principle role of QA protagonist. In reality, this current QA model is inherently flawed, for it relies on the subjective evaluation of individuals who have variable degrees of clinical experience, QA education and training, and oversight.
The large inter-technologist and inter-institutional QA variability is further compounded by the fact that the subjective methodology used for QA assessment is not recorded, tracked, or analyzed on a consistent and reliable basis. Thus, a method and apparatus, which is objective but user-specific, to automate the collection, storage, and analysis of QA metrics, and perform a QA assessment during medical imaging, on a consistent and reliable basis, is desired.